Senior MLOps Engineer, King

• We are interested in someone who has knowledge in operating ML and can assist ML engineers in designing and building robust end-to-end ML products
• Experience building API-first services and applications
• Experience building end-to-end data and ML pipelines
• Basic experience with TensorFlow, PyTorch or any other ML framework
• Experience working with solutions of major public cloud providers, preferably Google Cloud Platform
• M.S. degree or equivalent experience in Computer Science, Mathematics, Machine Learning, Data Science, Statistics, Operations Research
• Engineering skills in Python, including standard Backend practices such as proper documentation, library packaging, versioning, testing
• Experience orchestrating data pipelines e.g. using Airflow
• Containerizing workloads and running these on Kubernetes (GKE)
• Proficient with GCP data and ML products (e.g. BigQuery, Cloud Storage, Dataflow, Pub/Sub, Vertex AI)
• Self-starter with a great drive and a curious and inquisitive mind-set
• Willing to step outside your comfort zone to broaden your skills and learn new technologies
• Highly collaborative work style and open to share your knowledge, receiving and giving feedback, performing code reviews and so on
• Able to collaborate with cross-discipline teams on complex problems and be able to clearly communicate your ideas and solutions
• Comfortable working in a team split between Sweden and Spain

Desirable

• You will be joining a team that enables Machine Learning (ML) and Automation initiatives to support that goal
• We are building the tech foundations necessary for ML products to scale in a reliable way and stay maintainable long-term
• We are looking for a great teammate who can contribute to the backend engineering, data engineering and cloud engineering aspects of our stack
• We are using technologies such as Python, BigQuery, Airflow, Kubernetes, Google Cloud Platform, as well as internal tools built by our central ML Platform and Data Warehouse organizations
• ML modeling and data analysis will not be part of your daily tasks, though a basic understanding is expected to collaborate effectively with ML engineers
• Translate business needs to technical requirements, scope and build scalable solutions to support ML models and automation initiatives related to levels and
• Game experience
• Assess requirements from cross-disciplinary Game teams, and collaborate with them to build data and ML solutions
• Designing scalable, efficient, quality tools to inform operations and enable
• Business and game design decision-making